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Effect of Claims Feature Engineering on Insurance Loss Prediction

Abstract

Claims feature engineering involves transforming, selecting, and constructing relevant variables from insurance claims data to improve the performance of statistical and predictive models. Insurance loss prediction depends on the quality and relevance of information used to estimate future claim frequency, severity, and aggregate losses. Appropriate feature engineering may therefore improve the ability of actuarial and analytical models to identify patterns in claims experience and produce reliable insurance loss estimates. This study will examine the effect of claims feature engineering on insurance loss prediction. It will assess how different approaches to transforming and constructing claims-related variables influence the accuracy of predicted insurance losses. The study will also compare prediction results obtained from raw claims variables with those generated after applying feature engineering techniques to determine the extent to which engineered features improve loss prediction. The study will focus on claims feature engineering, insurance loss prediction, claims data, predictive variables, actuarial modelling, loss frequency, claim severity, feature selection, data transformation, and predictive accuracy. Statistical and actuarial techniques will be used to develop and evaluate insurance loss prediction models based on relevant claims characteristics. Feature engineering methods may include variable transformation, aggregation, interaction creation, categorical encoding, and selection of important claims-related variables. A quantitative research approach will be adopted for the study. Historical insurance claims data containing claim amounts, claim frequency, policy characteristics, exposure information, and other relevant variables will be analysed. Descriptive statistics, correlation analysis, feature selection techniques, regression modelling, predictive modelling, model validation, error analysis, and sensitivity analysis will be used to compare insurance loss predictions before and after feature engineering. Prediction accuracy will be evaluated using appropriate statistical performance measures. The study is expected to reveal that claims feature engineering may have a significant effect on insurance loss prediction. Well-designed engineered features may improve the ability of predictive models to identify relationships within claims data and reduce prediction errors, while poorly constructed or irrelevant features may have limited or adverse effects on model performance. The magnitude of the effect may depend on data quality, feature relevance, claims characteristics, model structure, and the feature engineering techniques applied. The study will be useful to actuaries, insurance companies, data scientists, claims analysts, pricing specialists, underwriters, regulators, and researchers. It may provide useful information for improving insurance loss prediction, strengthening actuarial modelling, enhancing claims analysis, and supporting more reliable pricing and reserving decisions. The findings may also assist insurers in identifying appropriate ways of transforming claims data for predictive applications. The study concludes that claims feature engineering is an important consideration in insurance loss prediction because the transformation and selection of claims variables can influence the performance and reliability of predictive models. It is therefore recommended that insurers apply appropriate feature engineering techniques, validate engineered variables carefully, and regularly assess model performance to ensure that transformed claims data provide meaningful and reliable information for insurance loss prediction.

Keywords: Claims feature engineering, insurance loss prediction, claims data, predictive modelling, actuarial modelling, feature selection, data transformation, insurance losses, claim frequency, claim severity, predictive accuracy, model validation, actuarial analysis, claims analytics, insurance risk prediction.